Last updated: 2019-11-03

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Knit directory: ~/Desktop/Ania_snakemake/raporty_preprocessing/analysis/

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Libraries

suppressPackageStartupMessages({
  library(plotly)
  library(readr)
  library(stringr)
  library(edgeR)
  library(stringr)
  library(pheatmap)
  library(purrr)
  library(scater)
  library(dplyr)
  library(reshape2)
  library(ggplot2)
  library(cowplot)
  library(Matrix)
  library(scran)
  library(Seurat)
  library(sctransform)
  library(readxl)
  library(DropletUtils)
  # library(LSD)
  library(CellMixS)
  library(tibble)
  library(ExperimentHub)
})
# Parameters
out_path <- here::here("output")
seed <- 1234

Load data

sc<-ExperimentHub()
Warning: `overscope_eval_next()` is deprecated as of rlang 0.2.0.
Please use `eval_tidy()` with a data mask instead.
This warning is displayed once per session.
Warning: `overscope_clean()` is deprecated as of rlang 0.2.0.
This warning is displayed once per session.
sce<-sc[["EH2259"]]
Warning: package 'muscData' is not available (for R version 3.6.0)
## Filter out genes that are not expressed in any cell
sce <- sce[which(rowSums(counts(sce) > 0) > 0), ]
dim(sce)
[1] 18890 29065
sce$Sample<-sce$ind
sce$dataset<-sce$ind

Calculate QC Metrics

# Mitochondrial genes
is.mito <- grepl("^MT", rowData(sce)$SYMBOL, ignore.case=T)
summary(is.mito)
   Mode   FALSE    TRUE 
logical   18812      78 
rownames(sce)[is.mito]
 [1] "MTOR"                   "MTHFR"                 
 [3] "MTFR1L"                 "MTF1"                  
 [5] "MTF2"                   "MTMR11_ENSG00000014914"
 [7] "MTX1"                   "MTR"                   
 [9] "MTA3"                   "MTIF2"                 
[11] "MTHFD2"                 "MTX2"                  
[13] "MTERFD2"                "MTMR14"                
[15] "MTRNR2L12"              "MTHFD2L"               
[17] "MTTP"                   "MTRR"                  
[19] "MTMR12"                 "MTX3"                  
[21] "MTRNR2L2"               "MTCH1"                 
[23] "MTRNR2L9"               "MTO1"                  
[25] "MTFR2"                  "MTHFD1L"               
[27] "MTRF1L"                 "MTURN"                 
[29] "MTERF"                  "MTPN"                  
[31] "MTMR8"                  "MTM1_ENSG00000171100"  
[33] "MTMR1_ENSG00000063601"  "MTCP1_ENSG00000214827" 
[35] "MTMR9"                  "MTMR7"                 
[37] "MTUS1"                  "MTFR1"                 
[39] "MTERFD1"                "MTDH"                  
[41] "MTBP"                   "MTSS1"                 
[43] "MTAP"                   "MTPAP"                 
[45] "MTRNR2L5"               "MTG1"                  
[47] "MTRNR2L8"               "MTCH2"                 
[49] "MTA2"                   "MTL5"                  
[51] "MTMR2"                  "MTERFD3"               
[53] "MTMR6"                  "MTIF3"                 
[55] "MTRF1"                  "MTHFD1"                
[57] "MTA1_ENSG00000182979"   "MTMR10"                
[59] "MTFMT"                  "MTHFS"                 
[61] "MTRNR2L4"               "MT3"                   
[63] "MT2A"                   "MT1E"                  
[65] "MT1M"                   "MT1A"                  
[67] "MT1F"                   "MT1G"                  
[69] "MT1H"                   "MT1X"                  
[71] "MTSS1L"                 "MTHFSD"                
[73] "MTRNR2L1"               "MTMR4"                 
[75] "MTRNR2L3"               "MTG2"                  
[77] "MTMR3"                  "MTFP1"                 
sce <- calculateQCMetrics(sce, feature_controls=list(Mt = is.mito),  percent_top=c(20,50,100,200))
plotHighestExprs(sce,n=20)

Filtering

# Find outlier
# Plot filters

plotFilters <- function( sce, var="log10_total_counts", split_by="Sample", nrow=NULL,
                         nmads=c(2,3,5), lt=c("dashed","dotted","dotdash"), xscale="free" ){
  CD <- as.data.frame(colData(sce))
  if(!(var %in% colnames(CD))) stop(paste("`var`",var,"is not in `colData(sce)`!"))
  if(!is.null(split_by) && !(split_by %in% colnames(CD))){
    stop(paste("`split_by`",split_by,"is not in `colData(sce)`!"))
  }
  library(ggplot2)
  library(cowplot)
  d <- CD[,var,drop=F]
  if(!is.null(split_by)) d$dataset <- CD[[split_by]]
  p <- ggplot(d, aes_string(x=var)) + geom_histogram(color="darkblue", bins=30)
  if(xscale!="free"){
    if(xscale!="fixed"){
      if(xscale>1 && xscale%%1==0){
        xq <- .tmads(d[[var]], xscale)
        xr <- range(d[[var]],na.rm=T)
        xq <- c(max(xq[1],xr[1]), min(xq[2],xr[2]))
      }else{
        if(xscale<=1 & xscale>0){
          xscale <- (1-xscale)/2
          xq <- quantile(d[[var]], probs=c(xscale,1-xscale), na.rm=T)
        }else{
          stop("Wrong `xscale` value!")
        }
      }
      p <- p + xlim(xq[1], xq[2])
    }
  }

  if(!is.null(split_by)){
    if(is.null(nrow)) nrow <- ceiling(length(unique(d$dataset))/3)
    p <- p + facet_wrap(~dataset, scales=ifelse(xscale=="free","free","free_y"), nrow=nrow)
    for(ds in unique(d$dataset)){
      for(i in 1:length(nmads)){
        ma <- .tmads(d[which(d$dataset==ds),var], nmads[i])
        df2 <- data.frame(xint=as.numeric(ma), dataset=rep(ds,2))
        p <- p + geom_vline(data=df2, aes(xintercept=xint), linetype=lt[i])
      }
    }
  }else{
    for(i in 1:length(nmads)){
      df2 <- data.frame(xint=as.numeric(.tmads(d[[var]], nmads[i])))
      p <- p + geom_vline(data=df2, aes(xintercept=xint), linetype=lt[i])
    }
  }
  p
}
.tmads <- function(x, nbmads=2.5){
  x2 <- nbmads*median(abs(x-median(x)))
  median(x)+c(-x2,x2)
}


plotFilters(sce)

plotFilters(sce, "log10_total_features_by_counts")

plotFilters(sce, "pct_counts_Mt", xscale=0.98)
Warning: Removed 291 rows containing non-finite values (stat_bin).
Warning: Removed 16 rows containing missing values (geom_bar).
Warning: Removed 1 rows containing missing values (geom_vline).

Warning: Removed 1 rows containing missing values (geom_vline).

Warning: Removed 1 rows containing missing values (geom_vline).

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Warning: Removed 1 rows containing missing values (geom_vline).

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Warning: Removed 2 rows containing missing values (geom_vline).
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Warning: Removed 1 rows containing missing values (geom_vline).

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Warning: Removed 1 rows containing missing values (geom_vline).

Warning: Removed 1 rows containing missing values (geom_vline).

Warning: Removed 1 rows containing missing values (geom_vline).

Warning: Removed 1 rows containing missing values (geom_vline).

#Filtering
sce$total_counts_drop <- isOutlier(sce$total_counts, nmads = 2.5,
                                   type = "both", log = TRUE, batch=sce$dataset)

sce$total_features_drop <- isOutlier(sce$total_features_by_counts, nmads = 2.5,
                                     type = "both", log = TRUE, batch=sce$dataset)

sce$mito_drop <- sce$pct_counts_Mt > .5 &
  isOutlier(sce$pct_counts_Mt, nmads = 2.5, type = "higher", batch=sce$dataset)

sce$isOutlier <- sce$total_counts_drop | sce$total_features_drop | sce$mito_drop
# quality plot
# Find outlier
outlierPlot <- function(cd, feature, aph=NULL, logScale=FALSE, show.legend=TRUE){
  if(is.null(aph)) aph <- paste0(feature, "_drop")
  if(!(aph %in% colnames(cd))) aph <- NULL
  p <-  ggplot(as.data.frame(cd), aes_string(x = feature, alpha = aph)) +
    geom_histogram(show.legend=show.legend)
  if(!is.null(aph)) p <- p + scale_alpha_manual(values = c("TRUE" = 0.4, "FALSE" = 1))
  if(logScale) p <- p + scale_x_log10()
  p
}

plQCplot <- function(cd, show.legend=TRUE){
  ps <- lapply(split(cd,cd$dataset), sl=show.legend, FUN=function(x,sl){
    list( outlierPlot( x, "total_counts", logScale=T, show.legend=sl),
          outlierPlot( x, "total_features_by_counts", "total_features_drop",
                       logScale=T, show.legend=sl),
          outlierPlot( x, "pct_counts_Mt", "mito_drop", show.legend=sl)
    )
  })
  plot_grid( plotlist = do.call(c, ps),
             labels=rep(basename(names(ps)), each=length(ps[[1]])),
             ncol=length(ps[[1]]),
             label_x=0.5 )
}

plQCplot(colData(sce), show.legend=FALSE)
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(colData(sce) %>% as.data.frame, aes(x=total_features_by_counts, y=total_counts, colour=pct_counts_Mt)) + geom_point() + facet_wrap(~Sample) +geom_density_2d(col="white") + scale_x_sqrt() + scale_y_sqrt()

ggplot(colData(sce) %>% as.data.frame, aes(x=total_features_by_counts, y=pct_counts_Mt)) + geom_point() + facet_wrap(~Sample) +geom_density_2d(col="white")

# Check outlier
mets <- c("total_counts_drop","total_features_drop","mito_drop")
sapply(mets, FUN=function(x){ sapply(mets, y=x, function(x,y){ sum(sce[[x]] & sce[[y]]) }) })
                    total_counts_drop total_features_drop mito_drop
total_counts_drop                 757                 523         4
total_features_drop               523                1070         6
mito_drop                           4                   6       691
nbcells <- cbind(table(sce$Sample),table(sce$Sample[!sce$isOutlier]))
colnames(nbcells) <- c("cells total","cells after filtering")
nbcells
     cells total cells after filtering
101         2390                  2314
107         1286                  1257
1015        5841                  5573
1016        4178                  3827
1039        1349                  1144
1244        4005                  3732
1256        4726                  4427
1488        5290                  4803
layout(matrix(1:2,nrow=1))
LSD::heatscatter( sce$total_counts, sce$total_features_by_counts, xlab="Total counts", ylab="Non-zero features", main="",log="xy")
w <- which(!sce$isOutlier)
LSD::heatscatter( sce$total_counts[w], sce$total_features_by_counts[w], xlab="Total counts", ylab="Non-zero features", main="Filtered cells",log="xy")

# summary of cells kept
cct <- table(sce$isOutlier, sce$Sample)
row.names(cct) <- c("Kept", "Filtered out")
cct
              
                101  107 1015 1016 1039 1244 1256 1488
  Kept         2314 1257 5573 3827 1144 3732 4427 4803
  Filtered out   76   29  268  351  205  273  299  487
# drop outlier cells
sce <- sce[,!sce$isOutlier]
# require count > 1 in at least 20 cells
sce <- sce[which(rowSums(counts(sce)>1)>=20),]
dim(sce)
[1]  5722 27077
#  Sample filtering
sce<-sce[,!sce$Sample %in% c("101","107")]
plQCplot(colData(sce), show.legend=FALSE)
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

dim(sce)
[1]  5722 23506
table(sce$dataset)

1015 1016 1039 1244 1256 1488 
5573 3827 1144 3732 4427 4803

Normalization

# Scater
set.seed(1000)
clusters <- quickCluster(sce, use.ranks=FALSE)
table(clusters)
clusters
   1    2    3    4    5    6    7    8    9   10   11   12 
2900 1844  351 1720 1862 5199 2413 1365 1364 2758 1550  180 
sce <- computeSumFactors(sce, min.mean=0.1, cluster=clusters) ##cluster information added
sce <- scater::normalize(sce)

Integration

# create SeuratObject
seurat <- CreateSeuratObject(counts = counts(sce), meta.data = as.data.frame(colData(sce)), min.cells = 0, min.features = 0, project = "10X", names.delim = ".")
Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')
# normalize, find variable genes, and scale
sl <- lapply(unique(as.character(seurat@meta.data$dataset)), FUN=function(x){
  x <- NormalizeData(SubsetData(seurat, cells=which(seurat@meta.data$dataset==x)))
  ScaleData(x)
  x <- FindVariableFeatures(x, verbose=F)
  # use non-standardized variance
  v <- x@assays$RNA@meta.features[["variance"]]
  VariableFeatures(x) <- row.names(x@assays$RNA@meta.features)[order(v, decreasing=TRUE)[1:500]]
  x
})
Centering and scaling data matrix
Centering and scaling data matrix
Centering and scaling data matrix
Centering and scaling data matrix
Centering and scaling data matrix
Centering and scaling data matrix
# find anchors & integrate
anchors <- FindIntegrationAnchors(sl)
Computing 2000 integration features
Scaling features for provided objects
Finding all pairwise anchors
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
    Found 11504 anchors
Filtering anchors
    Retained 5994 anchors
Extracting within-dataset neighbors
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
    Found 12079 anchors
Filtering anchors
    Retained 5414 anchors
Extracting within-dataset neighbors
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
    Found 13101 anchors
Filtering anchors
    Retained 6510 anchors
Extracting within-dataset neighbors
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
    Found 4875 anchors
Filtering anchors
    Retained 4060 anchors
Extracting within-dataset neighbors
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
    Found 4913 anchors
Filtering anchors
    Retained 4153 anchors
Extracting within-dataset neighbors
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
    Found 4783 anchors
Filtering anchors
    Retained 3917 anchors
Extracting within-dataset neighbors
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
    Found 12217 anchors
Filtering anchors
    Retained 6283 anchors
Extracting within-dataset neighbors
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
    Found 13226 anchors
Filtering anchors
    Retained 7070 anchors
Extracting within-dataset neighbors
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
    Found 13502 anchors
Filtering anchors
    Retained 6853 anchors
Extracting within-dataset neighbors
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
    Found 5150 anchors
Filtering anchors
    Retained 2753 anchors
Extracting within-dataset neighbors
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
    Found 10929 anchors
Filtering anchors
    Retained 5677 anchors
Extracting within-dataset neighbors
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
    Found 11714 anchors
Filtering anchors
    Retained 6475 anchors
Extracting within-dataset neighbors
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
    Found 12059 anchors
Filtering anchors
    Retained 6417 anchors
Extracting within-dataset neighbors
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
    Found 4706 anchors
Filtering anchors
    Retained 2673 anchors
Extracting within-dataset neighbors
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
    Found 12087 anchors
Filtering anchors
    Retained 6829 anchors
Extracting within-dataset neighbors
seurat <- IntegrateData(anchorset = anchors)
Merging dataset 4 into 2
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
Integrating data
Merging dataset 6 into 5
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
Integrating data
Merging dataset 2 4 into 5 6
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
Integrating data
Merging dataset 1 into 5 6 2 4
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
Integrating data
Merging dataset 3 into 5 6 2 4 1
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
Integrating data
# scale integrated data
DefaultAssay(object=seurat) <- "integrated"
seurat <- ScaleData(seurat, verbose=F)

Dimension reduction

seurat <- RunPCA(object = seurat, npcs = 30, verbose = FALSE)
seurat <- RunTSNE(object = seurat, perplexity = 30,reduction = "pca", dims = seq_len(20),
                  seed.use = seed, do.fast = TRUE, verbose = FALSE)
seurat <- RunUMAP(object = seurat, reduction = "pca", dims = seq_len(20),
                  seed.use = seed, verbose = FALSE,n.neighbors = 30, min.dist = 0.5)

Clustering

seurat <- FindNeighbors(object = seurat, reduction = "pca", dims = seq_len(20), verbose = FALSE)
for (res in c(0.1, 0.2, 0.4, 0.8, 1, 1.2, 2))
seurat <- FindClusters(object = seurat, resolution = res, random.seed = seed, verbose = FALSE)
seurat <- SetIdent(seurat, value="integrated_snn_res.0.2")

Convert seurat to sce

sce <- SingleCellExperiment(
  assays=list(
    counts=seurat@assays$RNA@counts,
    logcounts=seurat@assays$RNA@data
  ),
  colData=seurat@meta.data,
  reducedDims=lapply(seurat@reductions, FUN=function(x) x@cell.embeddings)
)

# assign cluster to sce
identical(colnames(sce), colnames(seurat))
[1] TRUE
sce$seurat_cluster <- seurat@meta.data$seurat_clusters
# Save data
saveRDS(sce, file = '~/Desktop/Ania_snakemake/aktualny_projekt_moj_github/characterize_batches/output/sce_khang.rds')
saveRDS(seurat, file = '~/Desktop/Ania_snakemake/aktualny_projekt_moj_github/characterize_batches/output/seurat_khang.rds')

sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats4    parallel  stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] ExperimentHub_1.10.0        AnnotationHub_2.16.0       
 [3] BiocFileCache_1.8.0         dbplyr_1.4.0               
 [5] tibble_2.1.1                CellMixS_1.0.0             
 [7] kSamples_1.2-8              SuppDists_1.1-9.4          
 [9] DropletUtils_1.3.13         readxl_1.3.1               
[11] sctransform_0.2.0           Seurat_3.0.0               
[13] scran_1.11.23               Matrix_1.2-17              
[15] cowplot_0.9.4               reshape2_1.4.3             
[17] dplyr_0.8.1                 scater_1.11.13             
[19] SingleCellExperiment_1.5.2  SummarizedExperiment_1.13.0
[21] DelayedArray_0.9.9          BiocParallel_1.17.18       
[23] matrixStats_0.54.0          Biobase_2.43.1             
[25] GenomicRanges_1.35.1        GenomeInfoDb_1.19.2        
[27] IRanges_2.17.4              S4Vectors_0.21.21          
[29] BiocGenerics_0.29.2         purrr_0.3.2                
[31] pheatmap_1.0.12             edgeR_3.25.3               
[33] limma_3.39.14               stringr_1.4.0              
[35] readr_1.3.1                 plotly_4.9.0               
[37] ggplot2_3.1.1              

loaded via a namespace (and not attached):
  [1] reticulate_1.12               R.utils_2.8.0                
  [3] tidyselect_0.2.5              RSQLite_2.1.1                
  [5] AnnotationDbi_1.46.0          htmlwidgets_1.3              
  [7] grid_3.6.0                    Rtsne_0.15                   
  [9] munsell_0.5.0                 codetools_0.2-16             
 [11] ica_1.0-2                     statmod_1.4.30               
 [13] future_1.12.0                 withr_2.1.2                  
 [15] colorspace_1.4-1              knitr_1.23                   
 [17] rstudioapi_0.10               ROCR_1.0-7                   
 [19] gbRd_0.4-11                   listenv_0.7.0                
 [21] Rdpack_0.11-0                 git2r_0.25.2                 
 [23] GenomeInfoDbData_1.2.0        bit64_0.9-7                  
 [25] rhdf5_2.27.15                 rprojroot_1.3-2              
 [27] xfun_0.7                      R6_2.4.0                     
 [29] ggbeeswarm_0.6.0              rsvd_1.0.0                   
 [31] locfit_1.5-9.1                bitops_1.0-6                 
 [33] assertthat_0.2.1              promises_1.0.1               
 [35] SDMTools_1.1-221.1            scales_1.0.0                 
 [37] beeswarm_0.2.3                gtable_0.3.0                 
 [39] npsurv_0.4-0                  globals_0.12.4               
 [41] workflowr_1.3.0               rlang_0.4.0                  
 [43] splines_3.6.0                 lazyeval_0.2.2               
 [45] BiocManager_1.30.4            yaml_2.2.0                   
 [47] backports_1.1.4               httpuv_1.5.1                 
 [49] tools_3.6.0                   gplots_3.0.1.1               
 [51] RColorBrewer_1.1-2            dynamicTreeCut_1.63-1        
 [53] ggridges_0.5.1                Rcpp_1.0.1                   
 [55] plyr_1.8.4                    zlibbioc_1.29.0              
 [57] RCurl_1.95-4.12               pbapply_1.4-0                
 [59] viridis_0.5.1                 zoo_1.8-5                    
 [61] ggrepel_0.8.0                 cluster_2.0.8                
 [63] fs_1.3.0                      magrittr_1.5                 
 [65] data.table_1.12.2             lmtest_0.9-37                
 [67] RANN_2.6.1                    fitdistrplus_1.0-14          
 [69] hms_0.4.2                     lsei_1.2-0                   
 [71] mime_0.6                      evaluate_0.13                
 [73] xtable_1.8-4                  gridExtra_2.3                
 [75] compiler_3.6.0                KernSmooth_2.23-15           
 [77] crayon_1.3.4                  R.oo_1.22.0                  
 [79] htmltools_0.3.6               later_0.8.0                  
 [81] tidyr_0.8.3                   DBI_1.0.0                    
 [83] MASS_7.3-51.4                 rappdirs_0.3.1               
 [85] R.methodsS3_1.7.1             gdata_2.18.0                 
 [87] metap_1.1                     igraph_1.2.4.1               
 [89] pkgconfig_2.0.2               vipor_0.4.5                  
 [91] dqrng_0.2.0                   XVector_0.23.2               
 [93] bibtex_0.4.2                  digest_0.6.19                
 [95] tsne_0.1-3                    rmarkdown_1.12               
 [97] cellranger_1.1.0              DelayedMatrixStats_1.5.2     
 [99] listarrays_0.2.0              curl_3.3                     
[101] shiny_1.3.2                   gtools_3.8.1                 
[103] nlme_3.1-139                  jsonlite_1.6                 
[105] Rhdf5lib_1.5.4                BiocNeighbors_1.1.12         
[107] viridisLite_0.3.0             pillar_1.4.0                 
[109] lattice_0.20-38               httr_1.4.0                   
[111] survival_2.44-1.1             interactiveDisplayBase_1.22.0
[113] glue_1.3.1                    png_0.1-7                    
[115] bit_1.1-14                    stringi_1.4.3                
[117] HDF5Array_1.11.11             blob_1.1.1                   
[119] BiocSingular_0.99.14          caTools_1.17.1.2             
[121] memoise_1.1.0                 irlba_2.3.3                  
[123] future.apply_1.2.0            ape_5.3